model.py

import torch
import torchvision
from torch import nn

def create_effnetb2(seed : int = 42, num_classes : int = 3):
  #1,2,3 create model , weights and transforms
  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
  transform = weights.transforms()
  model = torchvision.models.efficientnet_b2(weights = weights)

  # frezzing the base layers
  for param in model.parameters():
    param.requires_grad = False

  #5 updating the clasiifier head for our model
  torch.manual_seed(seed)
  model.classifier = nn.Sequential(
      nn.Dropout(p = 0.3, inplace = True),
      nn.Linear(in_features =  1408,out_features = num_classes)
  )
  return model, transform